gravel_calculatorGravel & Aggregate Calculator (Yards & Tons) — Calculate how much gravel or aggregate you need for a driveway, path, or garden bed. Enter area and depth to get cubic yards, cubic feet, and tons by density.
| Name | Required | Description | Default |
|---|---|---|---|
| areaSqFt | Yes | ||
| depthInches | Yes | ||
| densityTonsPerYard | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of explaining behavior. It discloses the output units (cubic yards, cubic feet, tons) and the key input concept (density), which is useful context. However, it does not detail assumptions, rounding, or error handling, though these are less critical for a simple read-only calculator.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with a title prefix and explanatory clause. It is concise and front-loaded with the purpose, but it repeats the tool title verbatim, which is a minor redundancy. Overall, it is efficient and every part contributes meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a calculator with three required parameters and no output schema, the description adequately covers both inputs and outputs. It explains what results to expect (cubic yards, cubic feet, tons) and the calculation basis (area, depth, density). It does not include detailed formulas, but that is not necessary for this tool type. The sibling context is addressed through the specific material type.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It mentions 'area and depth' and 'tons by density', mapping to the three required parameters. The parameter names (areaSqFt, depthInches, densityTonsPerYard) are self-explanatory, and the description adds the aggregation context, making the semantics clear enough for an agent to invoke correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: calculating how much gravel or aggregate is needed, with specific verbs ('Calculate') and a concrete resource ('gravel or aggregate'). It also lists use cases (driveway, path, garden bed), which differentiates it from sibling calculators like concrete_calculator, mulch_calculator, and topsoil_calculator.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool by listing common applications (driveway, path, garden bed). It does not explicitly mention alternatives or exclusions, but the specificity of 'gravel or aggregate' implicitly guides selection among the many sibling calculators.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many calculators occupy overlapping conceptual spaces, such as 'ai_roi_calculator' vs 'ai_automation_payback_calculator' and 'llm_self_host_vs_api_calculator' vs 'ai_build_vs_buy_calculator'. The boundaries between debt payoff, savings goal, and drawdown tools are also fuzzy, making it easy for an agent to select the wrong tool despite detailed descriptions.
Every tool follows the same <topic>_calculator pattern with lowercase snake_case, making the naming highly predictable and consistent. Even acronyms and numbers fit the pattern, so there is no mixing of conventions.
122 tools is an extreme number for a single MCP server, far exceeding the 50+ threshold for a severe mismatch. The tools span unrelated domains like AI costs, pet food, concrete, pizza dough, and turkey cooking, creating an unfocused kitchen-sink surface that overwhelms an agent's selection process.
The set covers many common calculator categories such as finance, construction, health, and AI costs, but several staple calculators are missing (e.g., BMI, tip, discount, simple interest, currency conversion). The AI cost cluster is over-saturated while other everyday calculations are absent, leaving minor but noticeable gaps.